Intelligent Recommender Systems in the COVID-19 Outbreak: The Case of Wearable Healthcare Devices

Mehrbakhsh Nilashi, Shahla Asadi, Rabab Ali Abumalloh, Sarminah Samad, Othman Ibrahim


Since social and environmental conditions have been changed dramatically in recent years, the spectrum of diseases caused by infections is also changing at a rapid pace.  The Internet of Things (IoT) is a new concept which enables users with wearable devices to monitor healthcare. Wearable devices have attracted a great deal of attention and popularity among academics and industry in the last decade. The potential of wearable technology has previously been proven in improving health efficiency and reducing healthcare costs. Wearable devices are believed to be of a very strong value, both for detection and for the tracking and control of the spread of infectious diseases such as COVID-19. Regardless of the importance of wearable devices, only a few number of studies have revealed the usefulness of wearable devices in COVID-19 outbreak. As many of people are not aware of wearable health devices advantages as a mean of tracking their health, as well as using online health communities in critical conditions with limited access to the doctors in hospitals, these types of healthcare technology should be widely introduced and advertised through online retailing shops to improve the individuals’ awareness and knowledge of these devices. This can be effectively done by knowledge sharing through social media and intelligent agents in online retailing websites. One of the intelligent systems in online retailing websites is recommendation agents which would be helpful in this situation. In case of wearable health devices to be recommended to the users the in the event of outbreaks, the recommendation systems in online retailing websites must be adaptable and aware to the event of outbreaks and consider the users’ demands in this situations. This study aims to investigate the advantages of wearable devices in the event of outbreaks or disasters for healthcare. In addition, the role of recommendation agents in introducing and recommending these devices is explored. Finally, this study reveals some shortcomings of current recommendation agents and provides appropriate solutions for effectiveness of these systems in the event of COVID-19 outbreak.


Recommender Systems, COVID-19 Outbreak, Wearable Healthcare Devices, Recommendation Agents, Internet of Things, Artificial Intelligence

Full Text:

Abstract PDF


Abumalloh, R., Ibrahim, O., & Nilashi, M. (2020). Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce. Technology in Society, 101253.

Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems Recommender systems handbook (pp. 217-253): Springer.

Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A. R., Knox, K., . . . Ibrahim, O. (2019). Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, 331-343.

Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2018). The application of internet of things in healthcare: a systematic literature review and classification. Universal Access in the Information Society, 1-33.

Allam, Z., Dey, G., & Jones, D. S. (2020). Artificial Intelligence (AI) Provided Early Detection of the Coronavirus (COVID-19) in China and Will Influence Future Urban Health Policy Internationally. AI, 1(2), 156-165.

Allam, Z., & Jones, D. S. (2020). On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Paper presented at the Healthcare.

Asadi, S., Abdullah, R., Safaei, M., & Nazir, S. (2019). An integrated SEM-Neural Network approach for predicting determinants of adoption of wearable healthcare devices. Mobile Information Systems, 2019.

Asadi, S., Rezvani, A., Khosravi, P., & Heidarzadeh, S. (2019). Trust matters: Adoption of wearable technology.

Asadi, S., Safaei, M., Yadegaridehkordi, E., & Nilashi, M. (2019). Antecedents of consumers’ intention to adopt Wearable Healthcare Devices. Journal of Soft Computing and Decision Support Systems, 6(2), 6-11.

Chiarugi, F., Karatzanis, I., Zacharioudakis, G., Meriggi, P., Rizzo, F., Stratakis, M., . . . Di Rienzo, M. (2008). Measurement of heart rate and respiratory rate using a textile-based wearable device in heart failure patients. Paper presented at the 2008 Computers in Cardiology.

Control, C. f. D., Prevention, & Diseases, N. C. f. I. (1994). Addressing emerging infectious disease threats: a prevention strategy for the United States: Centers for Disease Control and Prevention.

Davenport, A., Gura, V., Ronco, C., Beizai, M., Ezon, C., & Rambod, E. (2007). A wearable haemodialysis device for patients with end-stage renal failure: a pilot study. The Lancet, 370(9604), 2005-2010.

Eccleston, C., Blyth, F. M., Dear, B. F., Fisher, E. A., Keefe, F. J., Lynch, M. E., . . . Williams, A. d. C. (2020). Managing patients with chronic pain during the Covid-19 outbreak: considerations for the rapid introduction of remotely supported (e-health) pain management services. Pain.

Gedikli, F., & Jannach, D. (2013). Improving recommendation accuracy based on item-specific tag preferences. ACM Transactions on Intelligent Systems and Technology (TIST), 4(1), 1-19.

Giansanti, D., Macellari, V., & Maccioni, G. (2008). Telemonitoring and telerehabilitation of patients with Parkinson’s disease: health technology assessment of a novel wearable step counter. Telemedicine and e-Health, 14(1), 76-83.

Granado-Font, E., Flores-Mateo, G., Sorlí-Aguilar, M., Montaña-Carreras, X., Ferre-Grau, C., Barrera-Uriarte, M.-L., . . . Satué-Gracia, E.-M. (2015). Effectiveness of a Smartphone application and wearable device for weight loss in overweight or obese primary care patients: protocol for a randomised controlled trial. BMC Public Health, 15(1), 531.

Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: scientific research and commercially available devices. Healthcare informatics research, 23(1), 4-15.

Jannach, D., Karakaya, Z., & Gedikli, F. (2012). Accuracy improvements for multi-criteria recommender systems. Paper presented at the Proceedings of the 13th ACM conference on electronic commerce.

Jannach, D., Zanker, M., & Fuchs, M. (2014). Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations. Information Technology & Tourism, 14(2), 119-149.

Kapoor, A., Guha, S., Das, M. K., Goswami, K. C., & Yadav, R. (2020). Digital healthcare: The only solution for better healthcare during COVID-19 pandemic? Indian Heart Journal.

Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine.

Konty, K. J., Bradshaw, B., Ramirez, E., Lee, W.-N., Signorini, A., & Foschini, L. (2019). Influenza Surveillance Using Wearable Mobile Health Devices. Online Journal of Public Health Informatics, 11(1).

Lakkireddy, D. R., Chung, M. K., Gopinathannair, R., Patton, K. K., Gluckman, T. J., Turagam, M., . . . Lampert, R. (2020). Guidance for Cardiac Electrophysiology During the Coronavirus (COVID-19) Pandemic from the Heart Rhythm Society COVID-19 Task Force; Electrophysiology Section of the American College of Cardiology; and the Electrocardiography and Arrhythmias Committee of the Council on Clinical Cardiology, American Heart Association. Heart Rhythm.

Lee, Y.-D., & Chung, W.-Y. (2009). Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring. Sensors and Actuators B: Chemical, 140(2), 390-395.

Lin, C.-S., Hsu, H. C., Lay, Y.-L., Chiu, C.-C., & Chao, C.-S. (2007). Wearable device for real-time monitoring of human falls. Measurement, 40(9-10), 831-840.

Liu, X., & Aberer, K. (2013). SoCo: a social network aided context-aware recommender system. Paper presented at the Proceedings of the 22nd international conference on World Wide Web.

Moran, G., & Muzellec, L. (2017). eWOM credibility on social networking sites: A framework. Journal of Marketing Communications, 23(2), 149-161.

Nilashi, M., Bagherifard, K., Rahmani, M., & Rafe, V. (2017). A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers & industrial engineering, 109, 357-368.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014a). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879-3900.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014b). Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 60, 82-101.

Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.

Nilashi, M., Ibrahim, O., Ahmadi, H., Shahmoradi, L., Samad, S., & Bagherifard, K. (2018). A recommendation agent for health products recommendation using dimensionality reduction and prediction machine learning techniques. Journal of Soft Computing and Decision Support Systems, 5(3), 7-15.

Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507-520.

Nilashi, M., Ibrahim, O. B., Ithnin, N., & Zakaria, R. (2015). A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 19(11), 3173-3207.

Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70-84.

Nilashi, M., Samad, S., Yusuf, S. Y. M., & Akbari, E. (2020). Can complementary and alternative medicines be beneficial in the treatment of COVID-19 through improving immune system function? Journal of Infection and Public Health.

Nilashi, M., Yadegaridehkordi, E., Ibrahim, O., Samad, S., Ahani, A., & Sanzogni, L. (2019). Analysis of Travellers’ Online Reviews in Social Networking Sites Using Fuzzy Logic Approach. International Journal of Fuzzy Systems, 21(5), 1367-1378.

Panniello, U., Tuzhilin, A., & Gorgoglione, M. (2014). Comparing context-aware recommender systems in terms of accuracy and diversity. User Modeling and User-Adapted Interaction, 24(1-2), 35-65.

Petney, T. N. (2001). Environmental, cultural and social changes and their influence on parasite infections. International Journal for Parasitology, 31(9), 919-932.

Price-Smith, A. T. (2001). The health of nations: infectious disease, environmental change, and their effects on national security and development: Mit Press.

Radin, J. M., Wineinger, N. E., Topol, E. J., & Steinhubl, S. R. (2020). Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. The Lancet Digital Health.

Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems, 78, 641-658.

Rashidi, M., Hussin, A. R. C., & Nilashi, M. (2015). Entropy-based Ranking Approach for Enhancing Diversity in Tag-based Community Recommendation. Journal of Soft Computing and Decision Support Systems, 3(1), 1-7.

Samson, S. I., Lee, W.-N., Quisel, T., Foschini, L., Liska, J., MILLS, H. G., . . . Beal, A. C. (2018). Using Claims and Consumer Wearable Devices Data to Quantify Influenza-Related Outcomes among Type 2 Diabetes Patients—A Large Population Study: Am Diabetes Assoc.

Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., . . . Agha, R. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery.

Statista. (2020a).

Statista. (2020b).

Statista. (2020c). Share of consumers that agree wearable health devices are helpful in England as of 2018, by aspect helpful for.

Statista. (2020d). Share of individuals seeing benefits by using health wearable in Norway 2018, by type.

Torales, J., O’Higgins, M., Castaldelli-Maia, J. M., & Ventriglio, A. (2020). The outbreak of COVID-19 coronavirus and its impact on global mental health. International Journal of Social Psychiatry, 0020764020915212.

Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (2012). Context-aware recommender systems for learning: a survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), 318-335.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.